modeling and control a d-statcom with sugeno and mamdani fuzzy logic controller for voltage sag mitigation


Abstract— Now-a-days Fuzzy logic control techniques are being
used as a promising approach for the assuage of power quality
problems. Voltage sag is the most dominating problem in the context
of power quality problems. This paper presentation deals with the
study of Distribution Static Compensator (D-STATCOM) using
Sugeno Fuzzy Controller (SFC), Mamdani Fuzzy Controller (MFC),
and PI Controller to minimize the voltage sag. Subsequently, the
modeling of D-STATCOM was made with applying three control
techniques namely Sugeno Fuzzy Controller (SFC), Mamdani Fuzzy
Controller (MFC), and PI Controller respectively. Besides, the
simulation study was practiced using Matlab with fuzzy toolbox. The
outcomes demonstrated in terms of all phases Sugeno Fuzzy
Controller (SFC) revealed the best performance among three
methods. Infact, SFC extricated 96%and 95% voltage sag difficulties
where two other methods such as MFC and PI controlled
compensated 93%, 90%and 85 %, 80%, respectively at two phase
and three phase scenario though this difference was not significant at
single phase scenario. As a result, SFC and MFC linked to D-
STATCOM are to be hypnotized for voltage sag mitigation due to
their capability become fends quickly against loads fluctuation in the
distribution system.
Keywords— D-Statcom, PI controller, Sugeno Fuzzy Controller
(SFC), Mamdani Fuzzy Controller (MFC), Matlab.
I. INTRODUCTION
HE Flexible Alternative Current System technology is a
new area in power engineering. It introduces the modem
power electronic technology into traditional ac power
system and significantly enhances power system
controllability [1]. Power quality is certainly a major concern
in present era. It becomes important with
insertion of sophisticated device. Whose performance is very
sensitive to quality of power supply [2]. The wide usage of
nonlinear loads, such as personal computers, variable speed
drivers, and the other electronic equipments reduce voltage
sag. Power quality is a major problem in industrial and
commercial power system. Electronic equipments are very
sensitive loads against voltage sag. Voltage sag is a reduction
Mohamed Mohamed Khaleel was born in 09-07-1988, Misrata, Libya. He
is currently research scholar M.Sc. Eng, Dept. MEC E, UNS. His research
interest includes Power System Analysis, Power Electronics, Power Quality,
Control Systems, Renewable Energy, and Artificial Intelligence Systems.
(Mobile Phone: +218 92 7443529). Email: lykhaleel@gmail.com
Mohamed Adam Farag, was born in Zawitina, Libya. He is currently
pursuing M.E (Final Year) Mechanical Engineering. His research interests
include power electronics and power system.
between 10 and 90% in RMS
voltage with duration between 0.5 cycles and 1 min [3].
The PI controllers can be built easily and they can add
robustness to power systems. However, SFC and MFC are one
of the interesting fields where fuzzy theory can be effectively
applied in term of controller [4]. D-STATCOM will be used
based on a voltage-source converter, which able to control the
capacitor. The fuzzy logic controller has very attractive
features over conventional controllers. [5]. Those control
strategy are very important to the operation of D-STATCOM in
order to mitigate voltage sag in distribution system. In general,
D-STATCOM is kind of custom power device which has
capability of power compensation. In this paper, three of the
powerful and famous controller methods are used to control
the D-STATCOM (e.g. SFC, MFC, and PI control), are applied
in MATLAB Simulation.
II. VOLTAGE SOURCE CONVERTER (VSC)
A voltage-source converter is a power electronic device,
which can generate a sinusoidal voltage with any required
magnitude, frequency and phase angle. Voltage source
converters are commonly used in adjustable-speed drives, but
can also be used to mitigate voltage sag in distribution system.
The VSC is used to either completely replace the voltage or to
inject the ‘missing voltage’. The ‘missing voltage’ is the
difference between the nominal voltage and the actual. The
converter is normally based on some kind of energy storage,
which will supply the converter with a DC voltage [6].
Normally the VSC is not only used for voltage sag mitigation,
but also for other power quality issues, e.g. swell and
harmonics.
Fig.1 Circuit Diagram of Voltage Source Converter
Fig. 1 shows the DC source is connected in parallel with the
DC capacitor. This DC capacitor could be charged by a
battery source or could be recharged by the converter itself.
Modeling and Control a D-STATCOM with Sugeno
Fuzzy Controller (SFC) and Mamdani Fuzzy
Controller (MFC) For Voltage Sag Mitigation
Mohamed Mohamed Khaleel, and Mohamed Adam Faraj
T
2nd International Conference on Emerging Trends in Engineering and Technology (ICETET'2014), May 30-31, 2014 London (UK)
http://dx.doi.org/10.15242/IIE.E0514603 114

III. DISTRIBUTION STATIC COMENSATOR
Essentially, the D D-STATCOM system is comprised of
three main parts: a VSC, a set of coupling reactors and a
controller. The basic principle of a D-STATCOM installed in a
power system is the generation of a controllable AC voltage
source by a voltage source converter (VSC) connected to a
DC capacitor (DC energy storage device) [7].
.
Fig. 1 Structure of D-STATCOM
Fig.1 shows the shunt injected current Ish corrects the
voltage sag by adjusting the voltage drop across the system
impedance Zth. The value of Ish can be controlled by adjusting
the output voltage of converter [8]. The shunt injected current
Ish can be written as,
ISH=IS-IL
Where,
IS= (VTH-VL/ZTH)
Therefore
ISH= IL-IS=IL (VTH-VL/ZTH)
The complex power injection of the D-STATCOM can be
expressed as,
SSH= VL IL
It may be mentioned that the effectiveness of D-
STATCOM is correction voltage sag depends on the value of
ZTH or fault level on load bus. When the shunt injected
current ISH is kept in quadrature with VL, the desired voltage
correction can be achieved without injecting any active power
in the system.
IV. MODELING PICONTROLLER OF D-STATCOM
PI Controller is a feedback controller which drives the plant
to be controlled with a weighted sum of the error (difference
between output and desired set-point) and the integral of that
value. The controller input is an error signal obtained from the
reference voltage and the value RMS of the terminal voltage
measured. Such error is processed by a PI controller the
output is the angle δ, which is provided to the PWM signal
generator. It is important to note that in this case, indirectly
controlled converter, there is active and reactive power
exchange with the network simultaneously: an error signal is
obtained by comparing the reference voltage with the RMS
voltage measured at the load point.
The PI controller process the error signal generates the
required angle to drive the error to zero, i.e., the load RMS
voltage is brought back to the reference voltage. The PI
control block diagram is shown in Fig. 3
Fig.3 Simulink Model of PI Controller
V. FUZZY LOGIC CONTOLLER (FLC)
The performance of Fuzzy logic controller is well
documented for improvements of voltage sag. The function of
fuzzy logic controller is very useful since exact mathematical
model of it is not required. The fuzzy logic control system can
be divided into four main functional blocks namely
Knowledge base, Fuzzification, Inference mechanism and
Defuzzification, Rule base. In this paper, a fuzzy logic based
feedback controller is employed for controlling the voltage
injection of the proposed D-STATCOM.
Fig. 4 The Basic Elements of a FLC
The main idea underlying the fuzzy logic control is, to
highlight the issues involved, Fig. 1 shows the basic element
of FLC, which comprises four principal components: a
fuzzification, a Rule base, Inference engine, and
defuzzification [9].
1. The fuzzification interface involves the following
functions
a. Measure the values of input variables,
b. Performs a scale mapping that transfers the rang of
values of input variables, into corresponding universes
of discourse,
c.Performs the function of fuzzification that converts
input data into suitable linguistic values.

2. The Knowledge based comprises a knowledge the
application domain and attendant control goal. It consists
of a “data base” and a “linguistic (fuzzy) Control rule
base’’
a. The data base provides necessary definitions, which are
used to Defined linguistic control rules and fuzzy data
manipulations in fuzzy logic control
b.The Rule base characterizes the control goals and
control policy of the domain experts by means of set
linguistic control rules.
3. The decision-making logic is the kernel of fuzzy logic
control. It has the capability of simulating human
decision-making based on fuzzy concepts and inferring
fuzzy control action employing fuzzy implication and
the rule of inference fuzzy logic.
4. The defuzzification interface performs the following
functions:
a. A scale mapping, which converts the range of values
output variables into corresponding universe of
discourse
b. Defizzification, which yields a non-fuzzy control
action from an inferred fuzzy control action.
A. Control System With Mfc
The design of FCSs with Mamdani FC is usually
performed by heuristic means incorporating human skills and
experience, and it is often carried out by a model-free
approach. The immediate shortcoming resulted from the
model-free design and Fuzzy Controller tuning concerns the
lack of general-purpose design methods. Although the
performance indices of such control systems are generally
satisfactory, a major problem is the analysis of the structural
properties possessed by the FCSs including stability,
controllability, parametric sensitivity and robustness [10]. The
membership function is shown below.
Fig. 5 Input1 Membership Function of MFC in MATLAB
Fig. 6 Input2 Membership function of MFC in MATLAB
Fig. 7 Output Membership Function of MFC in MATLAB
MFC have two inputs and one output, the input consisting of
7 members and output fuzzy consists of 7 members. Where
the input variables in the range [-5 5], while the output
variable in the range [-10 10].
The Mama fuzzy logic is used; the max-min inference
method is applied in this paper. The relation surface between
inputs (e, de) and output (du) of SFL is shown in Figure 8.
Fig. 8 Control Surface of MFC

TABLE 1
RULE BASE OF MFC
B. Control System With Sfc
Sugeno fuzzy models represent fuzzy dynamic models or
fuzzy systems. This brings a twofold advantage. First, any
model-based technique (including a nonlinear one) can be
applied to the fuzzy dynamic models. Second, the controller
itself can be considered as a fuzzy system. Since the fuzzy
model of the nonlinear process is usually based on a set of
local linear models which are smoothly merged by the fuzzy
model structure, a natural and straightforward approach is to
design one local controller for each local model of the process
[10].
Fig. 9 Inpu1 Membership Function of SFC in MATLAB
Fig. 10 Input2 Membership Function of SFC in MATLAB
SFC consist of two input and one output, only the output in
the form of a constant SFC method and not arrange, namely
fuzzy {P, Z, N} with range [-1 1], while the output consists of
9 members that each member has a constant value. Output =
[A, B, C, D, E, F, G, H, I] where :
A = -1397, B = -1397, C = -1397, D = -30.42, E = -30.42,
F = -30.42, G = 1319, H = 1320, I = 1320.
The SFC is used; the max-min inference method is applied
in this paper. The relation surface between inputs (e, de) and
output (du) of SFL is shown in Figure 8.
Fig.11 Control Surface of SFC
Fig. 12 Rule Base of SFC
C. Methodology
To enhance the performance of distribution network,
D-STATCOM was connected to the distribution system. D-
STATCOM was designed using MATLAB with its Fuzzy
Logic Toolbox. Fig. 11 below shows the flow chart for
methodology:

Fig. 13 Flow Chart of Control Methodology
VI. D-STATCOM TEST SYSTEM
The performance of the designed D-STATCOM, as shown
in fig. 14 is evaluated using MATLAB with its Fuzzy Logic
Toolbox. Table 2 shows the values of parameters in
distribution system.
Fig. 14 Simulation Model of D-STATCOM
TABLE II
SYSTEM PARAMETERS
S. No System Quantities Standard
1 Voltage Source 15kV
2 Line Frequency 50HZ
3 Series Transformer 1:1
4 Load Impedance 0.36 ohm
5 DC Voltage source 2kV
6 Filter Inductance 3mH
VII. THE RESULT OF D-STATCOM
The result consists of distribution system without D-
STATCOM and with D-STATCOM. The D-STATCOM was
simulated by using MATLAB. The voltage say occur at the
time duration 0.5sec to 0.8sec.
Fig. 15 Single Phase Fault Scenario Result of Comparison Use the PI
Controller, SFC and MFC
The figure 15 illustrates the single fault scenario result of
absence of D-STATCOM method and comparison among
three other methods namely PI controller, SFC, and MFC.
Fig. 16 Two Phase Fault Scenario Result of Comparison Use the PI
The figure 16 illustrates the two fault scenario result of

Fig. 17 Three Phase Fault Scenario Result of Comparison Use the PI
The figure 17 illustrates the three fault scenario result of
VIII. CONCLUSION
In a nutshell, the simulation of D- STATCOM subsuming
SFC, MFC and PI was executed successfully. Therefore, the
accomplishment of D- STATCOM was analyzed perfectly.
The outcomes uncovered that the best performer SFC restored
96%, and 95% voltage sag difficulties where two other
methods such as MFC and PI controlled countervailed 93%,
90% and 85 %, 80%, respectively at two phase and three
phase scenario though this difference was not significant at
single phase scenario. Consequently, the result findings
suggested the usage of SFC and MFC integrated with D-
STATCOM for the mitigation of voltage sag problems owing
to their competency to riposte against loads in the distribution
system.
REFERENCES
[1] Adepoju, G. A., Komolafe, O.A. “Analysis and Modeling of Static
Synchronous Compensator (STATCOM): A comparison of Power
Injection and Current Injection Models in Power Flow Study,”
International Journal of Advanced Science and Technology Vol. 36, pp.
65–75, 2011.
[2] Janak B. P., Amitkumar S. “Modeling Analysis& Solution of Power
Quality Problems Using DVR & DSTATCOM,” IJSRD -International
Journal for Scientific Research & Development. Vol. 1, Issue 5, 2013.
[3] M. M. Khaleel “ Enhancement Power Quality with Sugeno-type Fuzzy
Logic and Mamdani-type Fuzzy Logic Base on DVR,” International
Journal of Advanced Research in Electrical, Electronics and
Instrumentation Engineering Vol. 3, Issue 4, April 2014
[4] Khaleel M. M., Khaleel A., Nizam M., Inayati “DVR with Artificial
Intelligent System for Voltage Sag Mitigation,” International Conference
on Advances Engineering Technology. Vol. 3, Issue 4, pp. 569-576,
2014.
[5] Gupta A., Sharma P.R. “Design and Simulation of Fuzzy Logic
Controller for DSTATCOM In Power System,” International Journal of
Engineering Science and Technology. Vol.3. No.10, pp.7815-7822,
2011.
[6] Bhattacharay Sourabh “Application of DSTATCOM Using
MATLAB/Simulation in Power System,” Research Journal Recent
Sciences. Vol.1, pp.430-433, 2012.
[7] Kumar K.S.,Venu N. “Improvement of DC link voltage in D-Statcom
based on Fuzzy Logic,” International Journal Of Engineering And
Science (IJES). Vol.2, pp.77-84, 2013.
[8] Shaik Khaja Nawaz, Shaik Hameed “Mitigation of Power Quality
Problems by Using D-statcom,”International Journal of Recent
Advances Engineering & Technology. Vol.1. pp. 2347-2812, 2013.
[9] Chuen Chien Lee “Fuzzy Logic Control System: FuzzyLogic Control-
partI,” IEEE Transaction on System, Man, and Cyberntics . Vol. 20,
March, 1999.
[10] Radu-Emil Precup , Hans Hellendoorn “Asurvey on Industrial
Applications of Fuzzy Control,” Elsevier Computer in Industry. pp. 213-
226. 2011.
Mohamed Mohamed Khaleel was born in 09-07-1988,
Misrata, Libya. He is currently research scholar M.Sc. Eng,
Dept. MEC E, UNS. His research interest includes Power
System Analysis, Power Electronics, Power Quality, Control
Systems, Renewable Energy, and Artificial Intelligence
Systems.
Mobile Phone: +218 92 7443529
Email: lykhaleel@gmail.com
Mohamed Adam Farag was born in Zawitina, Libya. He is
currently pursuing M.E (Final Year) Mechanical
Engineering. His research interests include power electronics
and power system.
Email: mohamedadam077@gmail.com

modeling and control a d-statcom with sugeno and mamdani fuzzy logic controller for voltage sag mitigation

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